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Although there exists some methods for coin detection and recognition, it is still a challenging task, especially for coins in natural scene. This paper proposed a method to detect and recognize the coins in natural scene. In the detection part, the Hough detection method is applied to detect the coin areas in the images. Then radius ratio, color feature and relative position constraints are used...
With the development of science and the improvement of social information, Biological Recognition Technology (BIT) is becoming increasingly important. Among them, the fingerprint identification technology has become the hot spot because of its feasibility and reliability. The traditional fingerprint identification method relies on matching feature points to get the similarity. Undoubtedly, this method...
Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour...
Ear recognition is gaining on popularity in recent years. The human ear are neither affected by expressions like faces are nor do need closer touching like finger-prints do. In this paper, a novel algorithm was proposed to do ear recognition using deep convolutional neural network and provide a visualization of the learned network. We design a convolutional neural network with three convolutional...
The limited underwater observation scenarios pose great challenges to the problem of object recognition from the low-resolution underwater images. This paper proposes a framework to explicitly learn the discriminative features from relatively low resolution images, by resorting to deep learning approaches and super-resolution method. Firstly, the framework tackles the problem of limited discriminative...
Nowadays, most vehicles are equipped with the display device and dashboard camera. The former provides drivers with a variety of road information such as the reverse image and satellite navigation, the latter ensures the interests of the driver when the accidents happened. A laser range finder and a webcam are used in the proposed system. This system can provide drivers with driving safety information...
It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve...
Automatic recognition of sign languages to help hearing impaired people is an area that has been explored for quite some time. However, this is still a practical problem due to the complexity involved making it a big challenge. The use of devices, such as the Kinect sensor, has been shown to be promising in gesture recognition. Therefore, is proposes an application for users of the Brazilian Sign...
This paper presents the results of the ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work aims at providing a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. At this competition, we proposed two independent classification...
Many Chinese characters are composed of sub-structures. Extracting and recognizing radicals or sub-structures are benefit to character recognition. This paper proposed a new handwritten Chinese character recognition method combining sub-structure recognition. Firstly, a density-based clustering method is adopted to find sub-structure patterns in sub-structure pattern discovering. Secondly, for multiple...
We propose a deep convolutional feature representation that achieves superior performance for word spotting and recognition for handwritten images. We focus on: -(i) enhancing the discriminative ability of the convolutional features using a reduced feature representation that can scale to large datasets, and (ii) enabling query-by-string by learning a common subspace for image and text using the embedded...
In this paper, we introduce a deep residual network to classify images of plankton. The Plankton Dataset, which consists of 30,336 plankton images of 121 classes, was used for a data science competition hosted on the Kaggle platform1. We finally achieved a top-5 accuracy of 95.8% and a nearly real-frame rate of 9.1ftps, which is close to the accuracy of the No.1 team (over 98%, 1.4ftps) 2 in the competition...
Automatic handwriting recognition of digits and digit strings, are of real interest commercially and as an academic research topic. Recent advances using neural networks and especially deep learning algorithms such as convolutional neural nets present impressive results for single digit recognition. Such results enable developing efficient tools for automatic mail sorting and reading amounts and dates...
Deep learning architectures based on convolutional neural networks (CNN) are very successful in image recognition tasks. These architectures use a cascade of convolution layers and activation functions. The setup of the number of layers and the number of neurons in each layer, the choice of activation functions and training optimization algorithm are very important. I present GPU implementation of...
In this paper, we propose a novel automatic traffic sign detection and recognition method. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost. Segmentation is implemented by the improved Grab cut via the detection information. Classification is defined as a multiclass categorization problem, which is solved by HOG feature and support vector machine...
Augmented reality combines real footage taken of a scene with virtual elements. However, most current methods rely on camera localisation and 3D reconstruction or point cloud generation in order to integrate augmented reality to the footage. In contrast, in this work we present a novel method to augment virtual reality to the scene based on the recognition of dominant planes in interior scenes. Our...
In this paper, we propose a principled framework for pornographic image recognition. Specifically, we present our definition of pornographic images, which characterizes the pornographic contents in images as the exposure of private body parts. As the private body parts often lie in local image regions, we model each image as a bag of local image patches (instances), and assume that for each pornographic...
Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small number of such projections. However, in practice, most reconstruction algorithms perform poorly at low measurement rates and are computationally very expensive...
Automatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates...
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality...
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